Agent skill · data analytics · personamanagmentlayer
data-science-expert
Expert-level data science, analytics, visualization, and statistical modeling. Use when the user mentions analytics, visualization, statistics, pandas, or NumPy, or when the task involves Data Analysis, Machine Learning, Data Visualization, or Feature Engineering.
Why this skill is useful
Provides a class for data cleaning and EDA with specific methods that enhance data preprocessing capabilities.
What it needs
Requires matplotlib, numpy, pandas, seaborn installed locally. About 5k tokens when loaded. Last updated 2026-03-30. 41 stars on the source repository.
What this skill does
Data Science Expert Expert guidance for data science, analytics, statistical modeling, and data visualization. Core Concepts Data Analysis Exploratory Data Analysis (EDA) Data cleaning and preprocessing Feature engineering Statistical inference Time series analysis A/B testing Machine Learning Supervised learning (classification, regression) Unsupervised learning (clustering, PCA) Model selection and validation Feature importance Hyperparameter tuning Ensemble methods Data Visualization Matplotlib, Seaborn, Plotly Statistical plots Interactive dashboards Storytelling with data Best practices for visualization Color theory and accessibility Data Cleaning and EDA Feature Engineering Time Series Analysis A/B Testing Best Practices Data Analysis Always explore data before modeling Check data quality and missing values Understand variable distributions Look for correlations and relationships Document data cleaning steps Validate assumptions Feature Engineering Create domain-specific features Test feature importance Avoid data leakage Use cross-validation for validation Document feature transformations Keep features interpretable Visualization Choose appropriate plot types Use clear labels and titles Consider color accessibility Avoid chartjunk Tell a story with data Make visualizations reproducible Anti-Patterns ❌ Not exploring data before modeling ❌ Ignoring data quality issues ❌ Data leakage in feature engineering ❌ Over-engineering features ❌ Misleading visualizations ❌ Not documenting analysis steps ❌ Ignoring business context Resources Pandas: https://pandas.pydata.org/ NumPy: https://numpy.org/ Scikit-learn: https://scikit-learn.org/ Seaborn: https://seaborn.pydata.org/ Plotly: https://plotly.com/python/
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills personamanagmentlayer/data-science-expert